Shifts in spatio-temporal fishing behaviour in the Canadian Pacific Halibut hook and line fishery as a result of a choke species
Bibliographic record
Abstract
Marine capture fisheries can be characterised by a combination of biological factors governing fish productivity and social factors governing fishers’ behaviours. Most fisheries science research has focused on the biological side of fisheries but few studies have attempted to combine quantitative analysis of fishing data with qualitative study of active fishing participants. Choke species, or species with a low quota allocation in contrast to their encounter rates, may present a particular challenge to management of multispecies fisheries. Choke species may restrict fishers’ ability to harvest other species, especially in the presence of at-sea monitoring, which prevents discarding of regulated species. This thesis combines a quantitative spatio-temporal analysis of the potential impact of a choke species on fishers’ behaviour in the British Columbian Pacific Halibut (Hippoglossus stenolepis) fishery with a qualitative analysis of fishers’ reactions to reductions in the quota of bycatch species, Yelloweye Rockfish (Sebastes ruberrimus). Novel criteria were developed and employed to determine if Yelloweye Rockfish acts as a choke species within the Pacific Halibut fishery. Inter-annual and seasonal fishing effort dynamics were studied in years “prior” to (2007-2015) and “post” (2016-2017) a large reduction in the Total Allowable Catch (TAC) for Yelloweye Rockfish. A cluster analysis based on a previous study was developed to identify individual skippers’ “fishing opportunities” (i.e., individual fishing grounds on a fine spatial scale) and track usage of fishing opportunities in “prior” and “post” years. Interviews with five active skippers were conducted to corroborate interpretation of the data analysis. Results indicate that fishers have been successful in reducing their Yelloweye Rockfish catches since the TAC reductions, through a series of avoidance fishing tactics, including shifting into deeper waters, seasonal shifts and the decreased utilization of areas with high proportion of Yelloweye Rockfish in the catch. Studies such as this can help understand the potential spatio-temporal impacts of further TAC reductions for Yelloweye Rockfish. More broadly, this thesis improves understanding of strategies that fishers can employ to comply with catch regulations in monitored multispecies fisheries, which may help improve design of management strategies in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".